realization that while biophysical, anatomical, and physiological data are necessary to understand the brain, they are, unfortunately, not sufficient."
What distinguishes the collection of models and systems called neural networks from "the enchanted loom" of neurons in the human brain? (Pioneer neuroscientist Sir Charles Sherrington coined this elegant metaphor a century ago.) That pivotal question will not be answered with one global discovery, but rather by the steady accumulation of experimental revelations and the theoretical insights they suggest.
Koch exemplifies the computational neuroscientist. Trained as a physicist as opposed to an experimental neurobiologist, he might be found at his computer keyboard creating code, in the laboratory prodding analog very-large-scale integrated (VLSI) chips mimicking part of the nerve tissue, or sitting at his desk devising schema and diagrams to explain how the brain might work. He addressed the Frontiers symposium on the topic "Visual Motion: From Computational Analysis to Neural Networks and Perception," and described to his assembled colleagues some of the "theories and experiments I believe crucial for understanding how information is processed within the nervous system." His enthusiasm was manifest and his speculations about how the brain might work provocative: ''What is most exciting about this field is that it is highly interdisciplinary, involving areas as diverse as mathematics, physics, computer science, biophysics, neurophysiology, psychophysics, and psychology. The Holy Grail is to understand how we perceive and act in this world—in other words, to try to understand our brains and our minds."
Among those who have taken up the quest are the scientists who gathered for the Frontiers symposium session on neural networks, most of whom share a background of exploring the brain by looking at the visual system. Terrence Sejnowski, an investigator with the Howard Hughes Medical Institute at the Salk Institute and University of California, San Diego, has worked on several pioneering neural networks and has also explored many of the complexities of human vision. Shimon Ullman at the Massachusetts Institute of Technology concentrates on deciphering the computations used by the visual system to solve the problems of vision. He wrote an early treatise on the subject over a decade ago and worked with one of the pioneers in the field, David Marr. He uses computers in the search but stated his firm belief that such techniques need to "take into account the known psychological and biological data." Anatomists, physiologists, and other neuroscientists have developed a broad body of knowledge about how the brain is wired together with its interconnected neu-